A procedure to detect general association based on concentration of ranks

Abstract

In modern high‐throughput applications, it is important to identify pairwise associations between variables and desirable to use methods that are powerful and sensitive to a variety of association relationships. We describe RankCover, a new non‐parametric association test of association between two variables that measures the concentration of paired ranked points. Here, “concentration” is quantified using a disk‐covering statistic similar to those employed in spatial data analysis. Considerations from the theory of Boolean coverage processes provide motivation, as well as anR2‐like quantity to summarize strength of association. Analysis of simulated and real datasets demonstrates that the method is robust and often powerful in comparison with competing general association tests. Copyright © 2017 John Wiley & Sons, Ltd.